Self-attention networks have proven to be of profound value for its strength of capturing
global dependencies. In this work, we propose to model localness for self-attention …

Self-attention networks (SANs) have drawn increasing interest due to their high
parallelization in computation and flexibility in modeling dependencies. SANs can be further …

Self-attention model has shown its flexibility in parallel computation and the effectiveness on
modeling both long-and short-term dependencies. However, it calculates the dependencies …

While much research has been done in text-to-image synthesis, little work has been done to
explore the usage of linguistic structure of the input text. Such information is even more …

Abstract Syntax parse trees are a method of representing sentence structure and are often
used to provide models with syntax information and enhance downstream task performance …

Self-attention networks (SAN) have attracted a lot of interests due to their high parallelization
and strong performance on a variety of NLP tasks, eg machine translation. Due to the lack of …

This paper presents a novel neural machine translation model which jointly learns
translation and source-side latent graph representations of sentences. Unlike existing …

The field of machine translation (MT), the automatic translation of written text from one
natural language into another, has experienced a major paradigm shift in recent years …

Most state-of-the-art neural machine translation (NMT) models progressively encode feature
representation in a bottom-up feed-forward fashion. This traditional encoding mechanism …

Benefitting from the rapid development of artificial intelligence (AI) and deep learning, the
machine translation task based on neural networks has achieved impressive performance in …